Diversity Preservation Research Articles

A Dual-Competition-Based Particle Swarm Optimizer for Large-Scale Optimization

Large-scale particle swarm optimization (PSO) has long been a hot topic due to the following reasons: Swarm diversity preservation is still challenging for current PSO variants for large-scale optimization problems, resulting in difficulties for PSO in balancing its exploration and exploitation. Furthermore, current PSO variants for large-scale optimization problems often introduce additional operators to improve their ability in diversity preservation, leading to increased algorithm complexity. To address these issues, this paper proposes a dual-competition-based particle update strategy (DCS), which selects the particles to be updated and corresponding exemplars with two rounds of random pairing competitions, which can straightforwardly benefit swarm diversity preservation. Furthermore, DCS confirms the primary and secondary exemplars based on the fitness sorting operation for exploitation and exploration, respectively, leading to a dual-competition-based swarm optimizer. Thanks to the proposed DCS, on the one hand, the proposed algorithm is able to protect more than half of the particles from being updated to benefit diversity preservation at the swarm level. On the other hand, DCS provides an efficient exploration and exploitation exemplar selection mechanism, which is beneficial for balancing exploration and exploitation at the particle update level. Additionally, this paper analyzes the stability conditions and computational complexity of the proposed algorithm. In the experimental section, based on seven state-of-the-art algorithms and a recently proposed large-scale benchmark suite, this paper verifies the competitiveness of the proposed algorithm in large-scale optimization problems.

Unveiling the complex networks of urban tree diversity research: A global perspective.

Ecosystem services offered by urban forests must be proactively managed to remain diverse and sustainable. Recent research findings deserve a systematic synthesis to elucidate inherent knowledge structures and dynamics. This study focused on the urban tree diversity theme from 2000 to 2022. Web of Science Core Collection database provided bibliometric details on academic publications. The data-driven quantitative analysis explored research quantities, emphasis, trends, patterns, linkages, and impacts by countries, institutions, authors, journals, and citations. Publications and research topics have expanded continually, with accelerated growth in recent years. Research activities, outputs and interactions demonstrated conspicuous spatial clustering. A few countries, institutions and researchers generated a notable proportion of publications. Their scholarly contributions were visualized in knowledge graphs as complex networks of nodes and inter-node links. Keyword analysis generated a network to indicate research hotspots and frontiers to steer and prioritize future studies. Recent findings affirmed that cities can harbor substantial tree diversity due to enhanced habitat heterogeneity and successful species adaptation. Aligning tree traits with environmental conditions and management objectives can improve benefits. Urbanization can filter tree traits to shape community assemblages through stressors: habitat degradation, fragmentation and loss, in conjunction with pollution, climate change, and introduced species. Diversity preservation strategies include protecting remnant natural vegetation, connecting green spaces, and restoring complex canopy geometry and biomass structure. The emerging frontiers are marked by modeling future species distributions, leveraging technologies like remote sensing, linking ecology with human values, and committing to community-based stewardship. Management can be upgraded by interdisciplinary perspectives integrating ecological science and social engagement. The findings highlight the need for biodiversity enrichment anchored by native species, trait-matched assemblages, adaptive policies, and community participation to create livable-green cities. This review synthesizes key advances in urban tree ecology and biodiversity research to inform the planning and stewardship of resilient urban forests.

Firefly algorithm-based LSTM model for Guzheng tunes switching with big data analysis

Guzheng tune progression involves intricately harmonizing melodic motif transitions. Effectively navigating this vast creative possibility space to expose musically consistent elaborations presents challenges. We develop a specialized large long short-term memory (LSTM) model for generating musically consistent Guzheng tune transitions. First, we propose novel firefly algorithm (FA) enhancements, e.g., adaptive diversity preservation and adaptive swim parameters, to boost exploration effectiveness for navigating the vast creative combinatorics when generating Guzheng tune transitions. Then, we develop a specialized stacked LSTM architecture incorporating residual connections and conditioned embedding vectors that can leverage long-range temporal dependencies in Guzheng music patterns, including unsupervised learning of concise Guzheng-specific melody embedding vectors via a variational autoencoder, encapsulating unique harmonic signatures from performance descriptors to provide style guidance. Finally, we use LSTM networks to develop adversarial generative large models that enable realistic synthesis and evaluation of Guzheng tunes switching. We gather an extensive 10+ hour corpus of solo Guzheng recordings spanning 230 musical pieces, 130 distinguished performing artists, and 600+ audio tracks. Simultaneously, we conduct thorough Guzheng data analysis. Comparative assessments against strong baselines over systematic musical metrics and professional listeners validate significant generation fidelity improvements. Our model achieves a 63 % reduction in reconstruction error compared to the standard FA optimization after 1000 iterations. It also outperforms baselines in capturing characteristic motifs, maintaining modality coherence with under 2 % dissonant pitch errors, and retaining desired rhythmic cadences. User studies further confirm the superior naturalness, novelty, and stylistic faithfulness of the generated tune transitions, with ratings close to real data.

Family Office Strategies in Art Investing: A Comprehensive Study Beyond Market Trends

Objective: The objective of this thesis is to explore the art investment strategies of family offices, focusing on their interactions with the art industry including artists, museums, galleries, and auction houses. The aim is to understand how these offices develop investment decisions that go beyond current market trends by integrating economic intelligence with cultural insights. This research provides practical guidance for family offices in art investment, fostering smarter, future-oriented market decisions while contributing to the art market's growth and cultural diversity preservation. Theoretical Framework:The theoretical framework of the thesis examines the evolution of family offices and their role in art investment, analyzing the dynamics and trends of the art market, the complexities of art investment including risks and rewards, and the importance of understanding art's value beyond economic terms. This framework sets the stage to explore how family offices balance financial goals with cultural and artistic appreciation in their investment strategies. Method：The methodology of the study employs a qualitative case study approach, focusing on the Cohen Family Office to explore decision-making processes and art value perceptions in family offices. Data is gathered from public sources, industry reports, and relevant literature, particularly regarding the Cohen Family Office's art investments. This analysis aims to understand how art investment decisions are influenced by art value perceptions and market dynamics within this specific family office. Results and Discussion: The study's results reveal that the Cohen Family Office demonstrates a profound understanding of art value, which influences their successful investment strategies, including strategic acquisitions and support of emerging artists. Their active engagement in the art industry enhances their investment decisions and art value perception. The office's approach balances artistic appreciation with financial planning, leading to significant cultural and economic impacts. The research highlights differences between personal family collections and strategically driven family office collections, emphasizing the latter's focus on long-term financial and cultural goals. Research Implications:The research offers valuable insights for family offices in art investment,emphasizing the importance of deep art value perception and strategic market engagement. It highlights how family offices can surpass market trends through informed decision-making, balancing economic and cultural values. The study contributes to a broader understanding of art market dynamics and family office roles, enhancing strategies for wealth management and cultural legacy preservation. Originality/Value:The originality and value of this thesis lie in its unique focus on family offices in art investment, providing a detailed case study approach and highlighting the importance of art value perception. This research offers fresh insights into combining cultural enrichment with financial acumen in art investment strategies, contributing significantly to the understanding of family offices’ roles in art market dynamics and the interplay between cultural legacy and wealth management.

A Large-Scale Multiobjective Particle Swarm Optimizer With Enhanced Balance of Convergence and Diversity.

Large-scale multiobjective optimization problems (LSMOPs) continue to be challenging for existing multiobjective evolutionary algorithms (MOEAs). The main difficulties are that: 1) the diversity preservation in both the objective space and the decision space needs to be taken into account when solving LSMOPs and 2) the existing learning structures in current MOEAs usually make the learning operators only coincidentally serve convergence and diversity, leading to difficulties in balancing these two factors. Therefore, balancing convergence and diversity in current MOEAs is difficult. To address these issues, this article proposes a multiobjective particle swarm optimizer with enhanced balance of convergence and diversity (MPSO-EBCD). In MPSO-EBCD, a novel velocity update structure for multiobjective particle swarm optimization is put forward, dividing the convergence, and diversity preservation operations into independent components. Following the proposed update structure, a weighted convergence factor is introduced to serve the convergence strategy, whilst a diversity preservation strategy is built to uniformly distribute the particles in the searched space based on a proposed multidimensional local sparseness degree indicator. By this means, MPSO-EBCD is able to balance convergence and diversity with specific parameters in independent operators. Experimental results on LSMOP benchmarks and a voltage transformer optimization problem demonstrate the competitiveness of the proposed algorithm compared to several state-of-the-art MOEAs.

Genetic profiles and phenotypic patterns in Taiwanese Phalaenopsis orchids: a two-step phenotype and genotype strategy using modified genetic distance algorithms.

This study establishes the first core collection (CC) for Taiwanese Phalaenopsis orchids to preserve genetic diversity and key traits essential for breeding and research, thereby enhancing breeding efficiency without the need for a large maintained parent population. We examined 207 commercial orchid cultivars from ten nurseries, characterized by two phenotypes and genotypic data from eight simple sequence repeat markers. Multiple imputation was applied to estimate missing phenotypes, minimizing potential uncertainties and ensuring the reliability of population structure analysis. Weighted k-means clustering identified seven distinct clusters, highlighting substantial genetic diversity. We proposed a two-step phenotype and genotype strategy and modified genetic distance algorithms to effectively preserve both phenotypic and genetic diversity while retaining key features. Consequently, 22 core accessions were selected, distributed across seven clusters, and representing the orchid germplasm collection. Our evaluation revealed significant diversity preservation, particularly in distinct characteristics and rare features, outperforming other methodologies. Pedigree background analysis further confirmed the representativeness of the CC in maintaining diverse genetic materials. We emphasized the importance of evaluating the CC by detailing the criteria and statistical analyses used to ensure the quality, representativeness, and effectiveness of the selected accessions. This study contributes to orchid breeding, conservation efforts, and sustainable agricultural practices by providing a valuable and comprehensive resource. In conclusion, our research establishes a groundbreaking CC, offering insights into the genetic landscape of Taiwanese Phalaenopsis orchids and highlighting potential advancements in breeding commercially desirable varieties.

CSGA: A DUAL POPULATION GENETIC ALGORITHM BASED ON MEXICAN CAVEFISH GENETIC DIVERSITY

Genetic algorithms (GAs) are search algorithms based on population genetics and natural selection concepts. Maintaining population variety in GAs is critical for ensuring global exploration and mitigating the risks of premature convergence. Rapid convergence to local optima is one such challenge in the application of genetic algorithms. To address this issue, we provide Cave-Surface GA (CSGA), an alternative method based on the Dual Population Genetic Algorithm and inspired by the genetic variety observed in Mexican cavefish. Through inter-population crossbreeding, CSGA increases diversity via a secondary population (Cave population) and facilitates the exchange of information between populations, effectively counteracting premature convergence. Several experiments are carried out utilizing benchmark instances of the Traveling Salesman Problem (TSP) obtained from TSPLIB, a well-known TSP problem library. Our experimental results over many TSP instances show that CSGA outperforms both classic GAs and other GAs that use diversity preservation techniques, such as Multipopulation GA (MPGA). CSGA has the potential to give promising solutions to challenging optimization issues like TSP.

Adaptive Neuroevolution With Genetic Operator Control and Two-Way Complexity Variation

Topology and weight evolving artificial neural network (TWEANN) algorithms optimize the structure and weights of artificial neural networks (ANNs) simultaneously. The resulting networks are typically used as policy models for solving control and reinforcement learning (RL) type problems. This paper presents a neuroevolution algorithm that aims to address the typical stagnation and sluggish convergence issues present in other neuroevolution algorithms. These issues are often caused by inadequacies in population diversity preservation, exploration/exploitation balance, and search flexibility. This new algorithm, called the Adaptive Genomic Evolution of Neural-Network Topologies (AGENT), builds on the neuroevolution of augmenting topologies (NEAT) concept. Novel mechanisms for adapting the selection and mutation operations are proposed to favorably control population diversity and exploration/exploitation balance. The former is founded on a fundamentally new way of quantifying diversity by taking a graph-theoretic perspective of the population of genomes and inter-genomic differences. Further advancements to the NEAT paradigm occur through the incorporation of variable neuronal properties and new mutation operations that uniquely allow both the growth and pruning of ANN topologies during evolution. Numerical experiments with benchmark control problems adopted from the OpenAI Gym illustrate the competitive performance of AGENT against standard RL methods and adaptive HyperNEAT, and superiority over the original NEAT algorithm. Further parametric analysis provides key insights into the impact of the new features in AGENT. This is followed by evaluation on an unmanned aerial vehicle collision avoidance problem where maneuver planning models are learnt by AGENT with 33% reward improvement over 15 generations.

Evolution of Modern Zoology

Technology has contributed to the development of zoology, and the electron microscope has played an important role in many biological discoveries, as have oscilloscopes Binary isotopes, radiometry, digital photography and satellite images, PCR and DNA, and GPS systems, computers, and many of the technologies that worked on Progress in research fields in various branches of zoology has even contributed to finding solutions to some of the problems facing the environment in order to develop terrestrial and marine resources and preserve biodiversity It was the discovery and identification of Mendelian genetics, the structure of DNA, the code, instinct and animal behaviour Primitive and hybrid species, parthenogenetic forms of life, and new, previously unknown kingdoms of chemomorphology all had a positive impact on the development of zoology This paper discusses the development of zoology in the modern era. What distinguishes zoology is its impact on environmental balance and diversity preservation issues Organisms, ranging from restriction enzymes, the discovery of cloning, genetic engineering, and genetic control of decomposition states. Understanding metabolic pathways supports the need to address these topics with careful review, as we will see in this paper.

A multi-modal multi-objective evolutionary algorithm based on dual decomposition and subset selection

Multi-modal multi-objective problems (MMOPs) arise frequently in real world applications, in which multiple Pareto-optimal solution sets (PSs) correspond to the same point on the Pareto front (PF). Diversity preservation is a crucial issue in multi-modal multi-objective evolutionary algorithms (MMOEAs), which aims at evolving the population toward the PF and equivalent PSs with a uniform distribution and good spread. In spite of many diversity preservation approaches in MMOEAs, most of them guarantee the diversity in decision space with a degraded performance in objective space. To address the above issues, this article proposes a MMEA based on dual decomposition and subset selection, which employes the weight vectors and grids to decompose both the objective and decision spaces. On the basis of dual decomposition, the mating selection are adaptively performed based on estimated optimization stages of current subproblem. Moreover, a subset with good diversity in both the objective and decision spaces are selected from the promising solutions ever collected during the evolution process. Experimental results on 22 benchmark problems demonstrate our advantages to maintain a more balanced diversity in both objective and decision spaces.© 2017 Elsevier Inc. All rights reserved.

Multi-objective decomposition evolutionary algorithm with objective modification-based dominance and external archive

In practice, the multi-objective optimization problem (MOP) is typically challenging in two aspects. On the one hand, its Pareto front has imbalanced search difficulties; on the other hand, its search space contains many dominance resistant solutions (DRSs). Decomposing a complicated MOP into several simple MOPs for collaborative optimization (M2M) has been acknowledged to be efficient in coping with the imbalanced search difficulty. Nevertheless, the convergence efficiency of the M2M-based multi-objective evolutionary algorithm (MOEA) is rarely investigated, especially on the MOP with DRSs. This paper reveals two convergence challenges faced by M2M-based MOEAs. Subsequently, a variant called MOEA/D-OMDEA is proposed to achieve better convergence efficiency without sacrificing advantages in diversity preservation. MOEA/D-OMDEA integrates a new relaxed dominance criterion, namely the OM-dominance criterion, into its environmental selection to alleviate the negative influence of inferior solutions (e.g., DRSs) as well as to better balance convergence and diversity. MOEA/D-OMDEA is compared with ten state-of-the-art MOEAs on two sets of MOP benchmarks with different characteristics and a real-world problem. Experimental results indicate that MOEA/D-OMDEA can significantly outperform the other ten competitors on these problems. In addition, this paper provides a thorough analysis of the effectiveness of each new algorithmic component and the sensitivity of each newly-introduced parameter.

Subspace segmentation based co-evolutionary algorithm for balancing convergence and diversity in many-objective optimization

With the increase of the objective dimension of optimization problems, the effect of comparing individuals through Pareto dominance relation drops sharply. While some algorithms enhance Pareto dominance via diversity preservation strategies, performance indicators, and reference vectors, many of them encounter difficulties in balancing the convergence and diversity of populations. Therefore, this paper proposes a subspace segmentation based co-evolutionary algorithm for balancing convergence and diversity in many-objective optimization. First, the decision space is divided into a convergence subspace and a diversity subspace, which are searched in the early and late stages to improve the population convergence and diversity, respectively. Second, a capacity adaptively adjusted archive is used to retain elite individuals with better convergence in the population, which is further used to mate with the population. Moreover, an indicator with penalty factor is proposed to retain the boundary individuals so as to maintain the population diversity. Comparing with 6 advanced many-objective evolutionary algorithms on 63 benchmark cases, the proposed algorithm obtains smallest IGD on 36 benchmark cases, the experimental results show that the proposed algorithm can balance convergence and diversity well and has exhibit competitiveness.

A many-objective optimization evolutionary algorithm based on hyper-dominance degree

Compared with multi-objective optimization, solving many-objective optimization problems usually require more strong selection pressure. However, too strong selection pressure usually leads to the loss of diversity, while insufficient selection pressure often results in the failure of convergence. How to control the selection pressure to balance convergence and diversity remains a challenge in many-objective optimization. To tackle this challenge, a many-objective optimization evolutionary algorithm based on the hyper-dominance degree is proposed in this paper. In the proposed algorithm, the convergence of each solution is quantified by hyper-dominance degree so that the convergence of the population can be controlled by setting a tolerance to screen solutions. To better balance the convergence and the diversity, a tolerance adjusting strategy is designed to control selection pressure during optimization, an improved reference vectors-based diversity preservation strategy is proposed to make the solutions well-distributed in the objective space, and a population reselection strategy based on hyper-dominance degree is proposed to further improve the convergence. The experimental results on various benchmark problems with up to 20 objectives verify that the proposed algorithm outperforms the state-of-the-art peer many-objective optimization algorithms.

A Correlation-Guided Layered Prediction Approach for Evolutionary Dynamic Multiobjective Optimization

When solving dynamic multiobjective optimization problems by evolutionary algorithms, the historical moving directions of some special points along the Pareto front, such as the center and knee points, are widely employed to predict the Pareto-optimal solutions (POS). However, special points may be impacted by certain individuals with a large direction deviation, and thus mislead the tracking of dynamic POS. To solve this issue, a correlation-guided layered prediction approach for solving dynamic multiobjective optimization problems is proposed in this paper, where multiple prediction models are integrated by considering the correlation of individuals’ moving directions. To be specific, the population is clustered into three subpopulations (i.e., high, mid, and low correlation) by correlation analysis to perform different prediction behaviors. The high correlation subpopulation aims to predict the moving direction via a linear prediction model. The mid correlation subpopulation is devoted to predicting the manifold change of POS by self-adaptively using the direction and length correction models. The diversity preservation is considered by the low correlation subpopulation. While the three subpopulations focus on different optimization tasks, they also cooperate to track the dynamic POS. The comprehensive experimental results on a variety of benchmark test problems demonstrate the superiority of the proposed approach, as compared with some state-of-the-art prediction-based dynamic multiobjective algorithms.

Premature convergence in morphology and control co-evolution: a study

This article addresses the co-evolution of morphology and control in evolutionary robotics, focusing on the challenge of premature convergence and limited morphological diversity. We conduct a comparative analysis of state-of-the-art algorithms, focusing on QD (Quality-Diversity) algorithms, based on a well-defined methodology for benchmarking evolutionary algorithms. We introduce carefully chosen indicators to evaluate their performance in three core aspects: task performance, phenotype diversity, and genotype diversity. Our findings highlight MNSLC (Multi-BC NSLC), with the introduction of aligned novelty to NSLC (Novelty Search with Local Competition), as the most effective algorithm for diversity preservation (genotype and phenotype diversity), while maintaining a competitive level of exploitability (task performance). MAP-Elites, although exhibiting a well-balanced trade-off between exploitation and exploration, fall short in protecting morphological diversity. NSLC, while showing similar performance to MNSLC in terms of exploration, is the least performant in terms of exploitation, contrasting with QN (Fitness-Novelty MOEA), which exhibits much superior exploitation, but inferior exploration, highlighting the effects of local competition in skewing the balance toward exploration. Our study provides valuable insights into the advantages, disadvantages, and trade-offs of different algorithms in co-evolving morphology and control.

Binary well placement optimization using a decomposition-based multi-objective evolutionary algorithm with diversity preservation

Binary well placement optimization using a decomposition-based multi-objective evolutionary algorithm with diversity preservation

A network community-based differential evolution for multimodal optimization problems

Multimodal optimization problems (MMOPs) aim to locate multiple global optima simultaneously, which requires a high diversity of solutions. As a diversity preservation strategy customized for evolutionary algorithms (EAs), niching has been extensively incorporated into many algorithms to deal with MMOPs. However, many existing niching techniques require pre-defined parameters to control the size or number of niches, making the algorithm sensitive to parameters. Taking individuals as nodes, the inverse distance of two individuals as edges, and the historical information of recent generations as node attributes, we can construct a population as a network, whose purpose is to divide niches through community detection without relying on sensitive parameters. Inspired by this idea, this paper proposes a network community-based differential evolution for multimodal optimization problems (NetCDEMMOPs). In NetCDEMMOPs, three strategies are proposed and work together. Firstly, the community detection-based niching (CDN) strategy constructs the population as an attribute network and automatically divides it into multiple niches without sensitive parameters. Secondly, the community elites-based updating (CEU) strategy allocates more evolutionary resources to elite individuals of communities, then predicts and updates these elites based on historical information. Finally, the poor individual remolding (PIR) strategy remolds the last-ranking individuals and guides them toward promising positions. After extensive experimentation with the widely adopted CEC'2013 test suit, the experimental results reveal the superiority of NetCDEMMOPs over many successful or recent algorithms for MMOPs.

A Multi-Objective Optimization Method for a Tractor Driveline Based on the Diversity Preservation Strategy of Gradient Crowding

This study presents a multi-objective optimization method for a tractor driveline based on the diversity maintenance strategy of gradient crowding. The objective was to address the trade-off between high power and low fuel consumption rates in a tractor driveline by optimizing the distribution of driveline ratios, aiming to enhance overall driving performance and reduce fuel consumption. This method introduces a strategy for evaluating gradient crowding to reduce non-inferior solution sets during selection to ensure the uniform and wide distribution of solutions while maintaining population diversity. The transmission ratio of a tractor is optimized by varying the input of the transmission ratios in each gear, constraining the theoretical tractor driving rate, common transmission ratio, and drive adhesion limit, and introducing the diversity maintenance strategy of gradient crowding. The goal is to reduce the loss rate of driving power and specific fuel consumption as much as possible. The analysis results demonstrate that the GC_NSGA-II algorithm, incorporating the evaluation strategy of gradient crowding, achieves greater diversity and a more uniform distribution in the front end. After verifying the algorithm, the optimized tractor showed a reduction of 41.62 (±S.D. 0.44)% in the theoretical loss rate of driving power and 62.8 (±S.D. 0.56)% in the loss rate of specific fuel consumption, indicating that the tractor’s drive performance significantly improved, accompanied by a substantial reduction in the fuel consumption rate. These findings affirm the feasibility of the proposed optimization method and provide valuable research insights for enhancing the overall performance of tractors.

3D reconstruction of unlimited-size real-world porous media by combining a BicycleGAN-based multimodal dictionary and super-dimension reconstruction

Three-dimensional reconstruction from a single two-dimensional image is important in the field of numerical simulation of porous media (including sandstones, composite, and alloys). Owing to limitations of computer storage space, the algorithm itself, and GPU memory, traditional algorithms and increasingly popular deep-learning algorithms cannot reconstruct complete real-size core images. To solve this problem, we propose a generative adversarial network dictionary-based super-dimension (GANDB-SD) algorithm that uses a BicycleGAN model to establish eight multimodal mappings of small-size structures. These mapping relations are used as the mapping dictionary when the super-dimension reconstruction framework is used to reconstruct the real core images without size limitation. Based on this algorithm, we further propose a three-dimensional co-occurrence matrix function as the loss function for the first time and propose a diversity preservation strategy to solve the pattern repetition problem during reconstruction. We performed an experiment to reconstruct a 5123 image, in which the morphological parameters and reconstruction time of different algorithms were compared. Results show that the algorithm has advantages in reconstruction accuracy and time. We further reconstructed 10003 real cores, and the statistical and morphological similarities between the reconstructed and target structures proved that the proposed algorithm is competent in the task of reconstructing real-world cores with unlimited size. We remark that our approach opens the door to a new methodology that incorporating machine learning/deep learning techniques into conventional super-dimension methods to address 3D reconstruction of unlimited-size real-world porous media issues in a wide range of related fields.

Annual Dynamics of Bird Community at a Coastal Wetland and Their Relation to Habitat Types: The Example of Beidagang Wetland, Northern China

In order to provide more scientific guidance for wetland bird protection, this study addressed the dynamics of the bird community sorted by ecotypes, classifications and threat categories from 2015 to 2019, and non-metric multidimensional scaling analysis, generalized additive models and the Mantel test were used to examine the relationships between bird communities and habitat types. The results showed that: (1) The abundance of birds peaked in 2017 at 88,258 individuals and then declined. Moreover, there was an inverse trend between species richness and abundance of birds, meaning greater abundance is associated with fewer species. (2) Swimming birds were dominant ecotypes and Anseriformes possessed the highest abundance. It was noteworthy that the abundance of critically endangered birds (Aythya baeri and Grus leucogeranus) and the species richness of endangered birds increased. (3) Building land and farmland had dominant impacts on the composition of bird community. Wading birds and birds in Gruiformes were significantly impacted by building land and farmland, and near threatened species were substantially influenced by farmland. Therefore, maintaining good connectivity between protected areas and surrounding areas is one of the best ways to effectively manage biodiversity of the target area. This research may provide a broader insight for coastal wetland bird habitat management and bird diversity preservation.